CiteScore: 1.18ℹ
CiteScore is the number of citations received in one year (Y), to documents published in the three previous years (Y-1, Y-2, Y-3), divided by the number of documents published in those same three years (Y-1, Y-2, Y-3).

Source Normalized Impact per Paper (SNIP): 1.380ℹSource Normalized Impact per Paper (SNIP):2015: 1.380SNIP measures contextual citation impact by weighting citations based on the total number of citations in a subject field.

SCImago Journal Rank (SJR): 0.570ℹSCImago Journal Rank (SJR):2015: 0.570SJR is a prestige metric based on the idea that not all citations are the same. SJR uses a similar algorithm as the Google page rank; it provides a quantitative and a qualitative measure of the journal’s impact.

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Modern computing systems are becoming increasingly diversified. Nowadays we hear about Systems of Systems, Cyber-Physical Systems, Ubiquitous Systems and so on. Many of these systems are embedded, many are subject to real-time constraints and most of them run an operating system. In this context, the term Computing Systems Engineering involves techniques related to a safe, correct and deadline-compliant development methodology for these systems.

Measurement and metrics play an important role in software development – they are one of the cornerstones making it an engineering discipline and they support working with customer data.

The use of software measurements dates back to the beginning of the discipline of software engineering and has evolved together with it from measurements of software properties to the measurement of both software and process properties and the measurement of customer behavior. In 2002 one of the important standards has been introduced in the area of software measurement – ISO/IEC 15939 (Software measurement processes) and has been revised in 2007 to include systems engineering (Software and systems engineering – measurement processes). This standard prescribes the processes of measurement collection alongside the definitions of basic vocabulary of metrology within the field of software engineering.

In the last decade, eLearning systems have become a standard tool in training both in academia (universities, schools...) and in the business (employee training programs, lifelong learning...). Currently, it is possible to observe a constant growth in the technological complexity of such systems in order to maximize the pedagogical utility of each new advance in technology. In this way, technological trends like recommender systems, augmented reality, situated and context-aware systems, mobile systems, Web 2.0 systems, videogame and entertainment technology, cloud computing, semantic technologies for e-learning, etc. find a direct application in the current e-learning arena. Thus, in order to face this complexity the development of eLearning software could greatly benefit from the adoption of well-established and domain-specific software architectures. Indeed, these architectures could constitute clear guidelines describing the decomposition of complex eLearning solutions into manageable components as well as the interplaying of these components to yield meaningful learning technological ecosystems.

Knowledge-based technologies provide a consistent and reliable basis to face the challenges for organization, manipulation and visualization of the data and knowledge, playing a crucial role as the technological basis of the development of a large number of information systems. On the other hand, software engineering comprises different processes and techniques that support the specification, development, and maintenance of software.

Most academic disciplines emphasize the importance of their general theories. Examples of general theories include the Big Bang theory, Maxwell’s equations, the theory of the cell, the theory of evolution, the theory of demand and supply, but among the general theories are also found theories with names such as the general theory of crime and the theory of marriage, both well-established within their respective fields.